In the field of telecommunication, solutions have been devised for providing relevant and potentially attractive services that have been adapted to different service consumers according to their interests and needs in different situations. These services can thus be customised for individual users depending on their user profiles and/or current situation. Some examples are advertising and personalised TV. Solutions have also been suggested for identifying groups or “clusters” of users belonging to different social networks, and for adapting various services to these user groups.
Differentiated adaptation of services for user groups in a communication network may be accomplished based on knowledge of social relationships between terminal users. This kind of information may be extracted from traffic data available in communication networks, i.e. information on executed calls and other sessions, using various methods and algorithms for social network analysis which have been developed recently. Further, it may be of interest for some parties to gain knowledge of various statistics and aspects related to social networks. Some examples of such known analysis techniques are “centrality methods”, i.e. finding the most central user in a social network, and “clique analysis”, i.e. finding a group of users which are closely related to each other in some respect. Any such methods and algorithms for analysing social networks are often generally referred to as “SNA (Social Network Analysis) algorithms”, which term will be used here as well.
Great amounts of traffic data are generally available from Charging Data Records (CDR) which are commonly generated and stored for the networks to support charging for executed calls and sessions. The traffic data may refer to voice calls, SMS (Short Message Service), MMS (Multimedia Message Service), game sessions and e-mails. In this description, the term “calls” is used for short to represent any type of communication between two parties, thus without limitation to voice calls. The traffic data may also contain further information on the calls related to the time of day, call duration, location and type of service used. Traffic data can also be obtained by means of various traffic analysing devices, such as Deep Packet Inspection (DPI) analysers and other traffic detecting devices, which can be installed at various communication nodes in the network.
This traffic data can thus be used to derive information on the social relations between different users, depending on the amount and type of communications these users have conducted with each other as well as time of day, duration and location when making their calls and sessions. Great efforts have been made to provide a mechanism for automatically identifying different groups or sets of users which are regarded as socially related, based on available traffic data. It has been generally recognised that a history of executed calls and sessions between different users can provide a basis for such social network analysis, basically assuming that two users having executed one or more calls can be regarded as socially related. Different criteria for calls may be defined for determining whether two users are deemed socially related, e.g. only calls exceeding 30 seconds to eliminate wrongly dialled numbers, only calls in both directions between two persons to eliminate support centres or the like, a minimum call frequency, and so forth.
However, many communication networks of today are quite extensive serving huge amounts of subscribers, maybe millions, that are typically active in such a large communication network. There is also a rapid increase of users and traffic in these networks. Therefore, it is a very complex and time-consuming process to detect and analyse millions of calls and sessions in order to identify different groups or sets of socially related users based on their previously executed communications.
For example, it may take several hours, days or even weeks for a processor system to execute SNA algorithms on a vast source of information on executed calls and sessions, such as traffic data, to identify a group of socially related users. This work also requires extensive memory resources for storing huge amounts of data. It is thus a problem that social network analysis of users in a communication network can be very complex and requires substantial storing, processor and computing resources, and that it also takes much time.